Theory of morphological neural networks

Jennifer L. Davidson; Gerhard X. Ritter

doi:10.1117/12.18085

1 July 1990 Theory of morphological neural networks

Proceedings Volume 1215, Digital Optical Computing II; (1990) https://doi.org/10.1117/12.18085
Event: OE/LASE '90, 1990, Los Angeles, CA, United States

Abstract

The theory of classical artificial neural networks has been used to solve pattern recognition problems in image processing that is different from traditional pattern recognition approaches. In standard neural network theory, the first step in performing a neural network calculation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for non-linearity of the network. This paper presents the fundamental theory for a morphological neural network which, instead of multiplication and summation, uses the non-linear operation of addition and maximum. Several basic applications which are distinctly different from pattern recognition techniques are given, including a net which performs a sieving algorithm.

Citation Download Citation

Jennifer L. Davidson and Gerhard X. Ritter "Theory of morphological neural networks", Proc. SPIE 1215, Digital Optical Computing II, (1 July 1990); https://doi.org/10.1117/12.18085

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